diff --git a/code/09_case-characteristics.R b/code/09_case-characteristics.R
new file mode 100644
index 0000000..7f84ee6
--- /dev/null
+++ b/code/09_case-characteristics.R
@@ -0,0 +1,187 @@
+# 09_user-navigation.R
+#
+# content: (1) Read data
+#          (2) Extract characteristics for cases
+#          (3) Select features for navigation behavior
+#          (4) Export data frames
+#
+# input:  results/haum/event_logfiles_2024-02-21_16-07-33.csv
+# output: results/haum/eventlogs_pre-corona_case-clusters.csv
+#
+# last mod: 2024-03-08
+
+# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
+
+source("R_helpers.R")
+
+#--------------- (1) Read data ---------------
+
+load("results/haum/eventlogs_pre-corona_cleaned.RData")
+
+# Select one year to handle number of cases
+dat <- dat[as.Date(dat$date.start) > "2018-12-31" &
+           as.Date(dat$date.start) < "2020-01-01", ]
+
+#--------------- (2) Extract characteristics for cases ---------------
+
+datcase <- aggregate(cbind(distance, scaleSize, rotationDegree) ~
+                     case, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)
+
+datcase$length <- aggregate(item ~ case, dat, length)$item
+
+eventtab <- aggregate(event ~ case, dat, table)["case"]
+eventtab$nmove <- aggregate(event ~ case, dat, table)$event[, "move"]
+eventtab$nflipCard <- aggregate(event ~ case, dat, table)$event[, "flipCard"]
+eventtab$nopenTopic <- aggregate(event ~ case, dat, table)$event[, "openTopic"]
+eventtab$nopenPopup <- aggregate(event ~ case, dat, table)$event[, "openPopup"]
+
+topictab <- aggregate(topic ~ case, dat, table)["case"]
+topictab$artist <- aggregate(topic ~ case, dat, table)$topic[, 1]
+topictab$details <- aggregate(topic ~ case, dat, table)$topic[, 2]
+topictab$extra_info <- aggregate(topic ~ case, dat, table)$topic[, 3]
+topictab$komposition <- aggregate(topic ~ case, dat, table)$topic[, 4]
+topictab$leben_des_kunstwerks <- aggregate(topic ~ case, dat, table)$topic[, 5]
+topictab$licht_und_farbe <- aggregate(topic ~ case, dat, table)$topic[, 6]
+topictab$technik <- aggregate(topic ~ case, dat, table)$topic[, 7]
+topictab$thema <- aggregate(topic ~ case, dat, table)$topic[, 8]
+
+datcase <- datcase |>
+  merge(eventtab, by = "case", all = TRUE) |>
+  merge(topictab, by = "case", all = TRUE)
+
+rm(eventtab, topictab)
+
+datcase$ntopiccards <- aggregate(topic ~ case, dat,
+                             function(x) ifelse(all(is.na(x)), NA,
+                             length(na.omit(x))), na.action =
+                             NULL)$topic
+datcase$ntopics <- aggregate(topic ~ case, dat,
+                             function(x) ifelse(all(is.na(x)), NA,
+                             length(unique(na.omit(x)))), na.action =
+                             NULL)$topic
+datcase$nitems <- aggregate(item ~ case, dat, function(x)
+                            length(unique(x)), na.action = NULL)$item
+datcase$npaths <- aggregate(path ~ case, dat, function(x)
+                            length(unique(x)), na.action = NULL)$path
+datcase$vacation <- aggregate(vacation ~ case, dat,
+                              function(x) ifelse(all(is.na(x)), 0, 1),
+                              na.action = NULL)$vacation
+datcase$holiday <- aggregate(holiday ~ case, dat,
+                             function(x) ifelse(all(is.na(x)), 0, 1),
+                             na.action = NULL)$holiday
+datcase$weekend <- aggregate(weekdays ~ case, dat,
+                             function(x) ifelse(any(x %in% c("Saturday", "Sunday")), 1, 0),
+                             na.action = NULL)$weekdays
+datcase$morning <- aggregate(date.start ~ case, dat,
+                             function(x) ifelse(lubridate::hour(x[1]) > 13, 0, 1),
+                             na.action = NULL)$date.start
+
+dat_split <- split(dat, ~ case)
+dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
+dat_minmax <- dplyr::bind_rows(dat_list)
+
+datcase$min_time <- aggregate(min_time ~ case, dat_minmax, unique)$min_time
+datcase$max_time <- aggregate(max_time ~ case, dat_minmax, unique)$max_time
+
+datcase$duration <- datcase$max_time - datcase$min_time
+datcase$min_time <- NULL
+datcase$max_time <- NULL
+
+artworks <- unique(dat$item)[!unique(dat$item) %in% c("501", "502", "503")]
+datcase$infocardOnly <- pbapply::pbsapply(dat_split, check_infocards, artworks = artworks)
+
+# Clean up NAs
+datcase$distance <- ifelse(is.na(datcase$distance), 0, datcase$distance)
+datcase$scaleSize <- ifelse(is.na(datcase$scaleSize), 1, datcase$scaleSize)
+datcase$rotationDegree <- ifelse(is.na(datcase$rotationDegree), 0, datcase$rotationDegree)
+datcase$artist <- ifelse(is.na(datcase$artist), 0, datcase$artist)
+datcase$details <- ifelse(is.na(datcase$details), 0, datcase$details)
+datcase$extra_info <- ifelse(is.na(datcase$extra_info), 0, datcase$extra_info)
+datcase$komposition <- ifelse(is.na(datcase$komposition), 0, datcase$komposition)
+datcase$leben_des_kunstwerks <- ifelse(is.na(datcase$leben_des_kunstwerks), 0, datcase$leben_des_kunstwerks)
+datcase$licht_und_farbe <- ifelse(is.na(datcase$licht_und_farbe), 0, datcase$licht_und_farbe)
+datcase$technik <- ifelse(is.na(datcase$technik), 0, datcase$technik)
+datcase$thema <- ifelse(is.na(datcase$thema), 0, datcase$thema)
+datcase$ntopics <- ifelse(is.na(datcase$ntopics), 0, datcase$ntopics)
+datcase$ntopiccards <- ifelse(is.na(datcase$ntopiccards), 0, datcase$ntopiccards)
+
+#--------------- (3) Select features for navigation behavior ---------------
+
+# Features for navigation types for MTT:
+# - Scanning / Overviewing:
+#   * Proportion of artworks looked at is high
+#   * Duration per artwork is low: "ave_duration_item" / datcase$duration
+# - Exploring:
+#   * Looking at additional information is high
+# - Searching / Studying:
+#   * Proportion of artworks looked at is low
+#   * Opening few cards
+#   datcase$nflipCard / mean(datcase$nflipCard) or median(datcase$nflipCard) is low
+#   * but for most cards popups are opened:
+#   datcase$nopenPopup / datcase$nflipCard is high
+# - Wandering / Flitting:
+#   * Proportion of moves is high
+#   * Duration per case is low:
+#   datcase$duration / mean(datcase$duration) or median(datcase$duration)
+#   * Duration per artwork is low: "ave_duration_item" / datcase$duration
+
+dattree <- data.frame(case = datcase$case,
+                      PropItems = datcase$nitems / length(unique(dat$item)),
+                      SearchInfo = (datcase$nopenTopic +
+                                    datcase$nopenPopup) / datcase$length,
+                      PropMoves = datcase$nmove / datcase$length,
+                      PathLinearity = datcase$nitems / datcase$npaths,
+                      Singularity = datcase$npaths / datcase$length
+)
+
+# centrality <- pbapply::pbsapply(dattree$case, get_centrality, data = dat)
+# save(centrality, file = "results/haum/tmp_centrality.RData")
+load("results/haum/tmp_centrality.RData")
+
+dattree$BetweenCentrality <- centrality
+
+# Average duration per item
+dat_split <- split(dat[, c("item", "case", "path", "timeMs.start", "timeMs.stop")], ~ path)
+dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
+dat_minmax <- dplyr::bind_rows(dat_list)
+
+tmp <- aggregate(min_time ~ path, dat_minmax, unique)
+tmp$max_time <- aggregate(max_time ~ path, dat_minmax, unique, na.action = NULL)$max_time
+tmp$duration <- tmp$max_time - tmp$min_time
+tmp$case <- aggregate(case ~ path, dat_minmax, unique)$case
+
+dattree$AvDurItem <- aggregate(duration ~ case, tmp, mean)$duration
+
+rm(tmp)
+
+# Indicator variable if table was used as info terminal only
+dattree$InfocardOnly <- factor(datcase$infocardOnly, levels = 0:1,
+                               labels = c("no", "yes"))
+
+# Add pattern to datcase; loosely based on Bousbia et al. (2009)
+dattree$Pattern <- "Dispersion"
+dattree$Pattern <- ifelse(dattree$PathLinearity > 0.8, "Scholar",
+                          dattree$Pattern)
+dattree$Pattern <- ifelse(dattree$PathLinearity <= 0.8 &
+                          dattree$BetweenCentrality >= 0.5, "Star",
+                          dattree$Pattern)
+dattree$Pattern <- factor(dattree$Pattern)
+
+dattree$AvDurItemNorm <- normalize(dattree$AvDurItem)
+
+#--------------- (4) Export data frames ---------------
+
+save(datcase, dattree, file = "results/haum/dataframes_case_2019.RData")
+
+write.table(datcase,
+            file = "results/haum/datcase.csv",
+            sep = ";",
+            quote = FALSE,
+            row.names = FALSE)
+
+write.table(datcase,
+            file = "results/haum/dattree.csv",
+            sep = ";",
+            quote = FALSE,
+            row.names = FALSE)
+
diff --git a/code/09_user-navigation.R b/code/09_user-navigation.R
deleted file mode 100644
index b6f5b02..0000000
--- a/code/09_user-navigation.R
+++ /dev/null
@@ -1,481 +0,0 @@
-# 09_user-navigation.R
-#
-# content: (1) Read data
-#           (1.1) Read log event data
-#           (1.2) Extract additional infos for clustering
-#          (2) Clustering
-#          (3) Fit tree
-#          (4) Investigate variants
-#
-# input:  results/haum/event_logfiles_2024-02-21_16-07-33.csv
-# output: results/haum/eventlogs_pre-corona_case-clusters.csv
-#
-# last mod: 2024-03-06
-
-
-# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
-
-library(bupaverse)
-library(factoextra)
-
-#--------------- (1) Read data ---------------
-
-#--------------- (1.1) Read log event data ---------------
-
-load("results/haum/eventlogs_pre-corona_cleaned.RData")
-
-# Select one year to handle number of cases
-dat <- dat[as.Date(dat$date.start) > "2018-12-31" &
-           as.Date(dat$date.start) < "2020-01-01", ]
-
-#--------------- (1.2) Extract additional infos for clustering ---------------
-
-datcase <- aggregate(cbind(distance, scaleSize, rotationDegree) ~
-                     case, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)
-
-datcase$length <- aggregate(item ~ case, dat, length)$item
-
-eventtab <- aggregate(event ~ case, dat, table)["case"]
-eventtab$nmove <- aggregate(event ~ case, dat, table)$event[, "move"]
-eventtab$nflipCard <- aggregate(event ~ case, dat, table)$event[, "flipCard"]
-eventtab$nopenTopic <- aggregate(event ~ case, dat, table)$event[, "openTopic"]
-eventtab$nopenPopup <- aggregate(event ~ case, dat, table)$event[, "openPopup"]
-
-topictab <- aggregate(topic ~ case, dat, table)["case"]
-topictab$artist <- aggregate(topic ~ case, dat, table)$topic[, 1]
-topictab$details <- aggregate(topic ~ case, dat, table)$topic[, 2]
-topictab$extra_info <- aggregate(topic ~ case, dat, table)$topic[, 3]
-topictab$komposition <- aggregate(topic ~ case, dat, table)$topic[, 4]
-topictab$leben_des_kunstwerks <- aggregate(topic ~ case, dat, table)$topic[, 5]
-topictab$licht_und_farbe <- aggregate(topic ~ case, dat, table)$topic[, 6]
-topictab$technik <- aggregate(topic ~ case, dat, table)$topic[, 7]
-topictab$thema <- aggregate(topic ~ case, dat, table)$topic[, 8]
-
-datcase <- datcase |>
-  merge(eventtab, by = "case", all = TRUE) |>
-  merge(topictab, by = "case", all = TRUE)
-
-rm(eventtab, topictab)
-
-datcase$ntopiccards <- aggregate(topic ~ case, dat,
-                             function(x) ifelse(all(is.na(x)), NA,
-                             length(na.omit(x))), na.action =
-                             NULL)$topic
-datcase$ntopics <- aggregate(topic ~ case, dat,
-                             function(x) ifelse(all(is.na(x)), NA,
-                             length(unique(na.omit(x)))), na.action =
-                             NULL)$topic
-datcase$nitems <- aggregate(item ~ case, dat, function(x)
-                            length(unique(x)), na.action = NULL)$item
-datcase$npaths <- aggregate(path ~ case, dat, function(x)
-                            length(unique(x)), na.action = NULL)$path
-datcase$vacation <- aggregate(vacation ~ case, dat,
-                              function(x) ifelse(all(is.na(x)), 0, 1),
-                              na.action = NULL)$vacation
-datcase$holiday <- aggregate(holiday ~ case, dat,
-                             function(x) ifelse(all(is.na(x)), 0, 1),
-                             na.action = NULL)$holiday
-datcase$weekend <- aggregate(weekdays ~ case, dat,
-                             function(x) ifelse(any(x %in% c("Saturday", "Sunday")), 1, 0),
-                             na.action = NULL)$weekdays
-datcase$morning <- aggregate(date.start ~ case, dat,
-                             function(x) ifelse(lubridate::hour(x[1]) > 13, 0, 1),
-                             na.action = NULL)$date.start
-
-dat_split <- split(dat, ~ case)
-
-time_minmax_ms <- function(subdata) {
-  subdata$min_time <- min(subdata$timeMs.start)
-    if (all(is.na(subdata$timeMs.stop))) {
-      subdata$max_time <- NA
-    } else {
-       subdata$max_time <- max(subdata$timeMs.stop, na.rm = TRUE)
-    }
-  subdata
-}
-# TODO: Move to helper file
-
-dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
-dat_minmax <- dplyr::bind_rows(dat_list)
-
-datcase$min_time <- aggregate(min_time ~ case, dat_minmax, unique)$min_time
-datcase$max_time <- aggregate(max_time ~ case, dat_minmax, unique)$max_time
-
-datcase$duration <- datcase$max_time - datcase$min_time
-datcase$min_time <- NULL
-datcase$max_time <- NULL
-
-
-check_infocards <- function(subdata, artworks) {
-  infocard_only <- NULL
-  if(any(unique(subdata$item) %in% artworks)) {
-    infocard_only <- FALSE
-  } else {
-    infocard_only <- TRUE
-  }
-  as.numeric(infocard_only)
-}
-# TODO: Move to helper file
-
-artworks <- unique(dat$item)[!unique(dat$item) %in% c("501", "502", "503")]
-
-datcase$infocardOnly <- pbapply::pbsapply(dat_split, check_infocards, artworks = artworks)
-
-
-# Clean up NAs
-datcase$distance <- ifelse(is.na(datcase$distance), 0, datcase$distance)
-datcase$scaleSize <- ifelse(is.na(datcase$scaleSize), 1, datcase$scaleSize)
-datcase$rotationDegree <- ifelse(is.na(datcase$rotationDegree), 0, datcase$rotationDegree)
-datcase$artist <- ifelse(is.na(datcase$artist), 0, datcase$artist)
-datcase$details <- ifelse(is.na(datcase$details), 0, datcase$details)
-datcase$extra_info <- ifelse(is.na(datcase$extra_info), 0, datcase$extra_info)
-datcase$komposition <- ifelse(is.na(datcase$komposition), 0, datcase$komposition)
-datcase$leben_des_kunstwerks <- ifelse(is.na(datcase$leben_des_kunstwerks), 0, datcase$leben_des_kunstwerks)
-datcase$licht_und_farbe <- ifelse(is.na(datcase$licht_und_farbe), 0, datcase$licht_und_farbe)
-datcase$technik <- ifelse(is.na(datcase$technik), 0, datcase$technik)
-datcase$thema <- ifelse(is.na(datcase$thema), 0, datcase$thema)
-datcase$ntopics <- ifelse(is.na(datcase$ntopics), 0, datcase$ntopics)
-datcase$ntopiccards <- ifelse(is.na(datcase$ntopiccards), 0, datcase$ntopiccards)
-
-cor_mat <- cor(datcase[, -1], use = "pairwise")
-diag(cor_mat) <- NA
-heatmap(cor_mat)
-
-
-normalize <- function(x) {
-  (x - min(x)) / (max(x) - min(x))
-}
-# TODO: Move to helper file
-
-# Features for navigation types for MTT:
-# - Scanning / Overviewing:
-#   * Proportion of artworks looked at is high
-#   * Duration per artwork is low: "ave_duration_item" / datcase$duration
-# - Exploring:
-#   * Looking at additional information is high
-# - Searching / Studying:
-#   * Proportion of artworks looked at is low
-#   * Opening few cards
-#   datcase$nflipCard / mean(datcase$nflipCard) or median(datcase$nflipCard) is low
-#   * but for most cards popups are opened:
-#   datcase$nopenPopup / datcase$nflipCard is high
-# - Wandering / Flitting:
-#   * Proportion of moves is high
-#   * Duration per case is low:
-#   datcase$duration / mean(datcase$duration) or median(datcase$duration)
-#   * Duration per artwork is low: "ave_duration_item" / datcase$duration
-
-dattree <- data.frame(case = datcase$case,
-                      PropItems = datcase$nitems / length(unique(dat$item)),
-                      SearchInfo = datcase$nopenTopic + datcase$nopenPopup,
-                      PropMoves = datcase$nmove / datcase$length,
-                      PathLinearity = datcase$nitems / datcase$npaths,
-                      Singularity = datcase$npaths / datcase$length
-)
-
-dattree$SearchInfo <- ifelse(is.na(dattree$NumTopic), 0, dattree$NumTopic)
-
-get_centrality <- function(case, data) {
-
-  data$start <- data$date.start
-  data$complete <- data$date.stop
-
-  alog <- activitylog(data[data$case == case, ],
-                      case_id     = "case",
-                      activity_id = "item",
-                      resource_id = "path",
-                      timestamps  = c("start", "complete"))
-
-  net <- process_map(alog, render = FALSE)
-  inet <- DiagrammeR::to_igraph(net)
-
-  #c(igraph::centr_degree(inet, loops = FALSE)$centralization,
-  #  igraph::centr_degree(inet, loops = TRUE)$centralization,
-  #  igraph::centr_betw(inet)$centralization)
-  igraph::centr_betw(inet)$centralization
-}
-# TODO: Move to helper file
-
-centrality <- pbapply::pblapply(dattree$case, get_centrality, data = dat)
-centrality <- do.call(rbind, centrality)
-
-# save(centrality, file = "results/haum/tmp_centrality.RData")
-#load("results/haum/tmp_centrality.RData")
-
-dattree$BetweenCentrality <- unlist(centrality)
-
-# Average duration per item
-dat_split <- split(dat[, c("item", "case", "path", "timeMs.start", "timeMs.stop")], ~ path)
-dat_list <- pbapply::pblapply(dat_split, time_minmax_ms)
-dat_minmax <- dplyr::bind_rows(dat_list)
-
-tmp <- aggregate(min_time ~ path, dat_minmax, unique)
-tmp$max_time <- aggregate(max_time ~ path, dat_minmax, unique, na.action = NULL)$max_time
-tmp$duration <- tmp$max_time - tmp$min_time
-tmp$case <- aggregate(case ~ path, dat_minmax, unique)$case
-
-dattree$AvDurItem <- aggregate(duration ~ case, tmp, mean)$duration
-
-rm(tmp)
-
-# Indicator variable if table was used as info terminal only
-dattree$InfocardOnly <- factor(datcase$infocardOnly, levels = 0:1,
-                               labels = c("no", "yes"))
-
-# Add pattern to datcase; loosely based on Bousbia et al. (2009)
-dattree$Pattern <- "Dispersion"
-dattree$Pattern <- ifelse(dattree$PathLinearity > 0.8 & dattree$Singularity > 0.8, "Scholar",
-                          dattree$Pattern)
-dattree$Pattern <- ifelse(dattree$PathLinearity <= 0.8 &
-                          dattree$BetweenCentrality > 0.5, "Star",
-                          dattree$Pattern)
-dattree$Pattern <- factor(dattree$Pattern)
-# TODO: Get rid of PathLinearity and Singularity as features when I am
-# using Pattern?
-
-dattree$PathLinearity <- NULL
-dattree$Singularity <- NULL
-dattree$BetweenCentrality <- NULL
-
-summary(dattree)
-
-plot(dattree[, -1], pch = ".")
-
-par(mfrow = c(2,4))
-hist(dattree$AvDurItem, breaks = 50, main = "")
-hist(dattree$NumItems, breaks = 50, main = "")
-hist(dattree$NumTopic, breaks = 50, main = "")
-hist(dattree$NumPopup, breaks = 50, main = "")
-hist(dattree$PropMoves, breaks = 50, main = "")
-hist(dattree$PathLinearity, breaks = 50, main = "")
-hist(dattree$Singularity, breaks = 50, main = "")
-hist(dattree$BetweenCentrality, breaks = 50, main = "")
-
-#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-# Remove cases with extreme outliers
-# TODO: Do I want this???
-
-quantile(datcase$nopenTopic, 0.999)
-quantile(datcase$nopenPopup, 0.999)
-
-dattree <- dattree[!(dattree$NumTopic > 40 | dattree$NumPopup > 40), ]
-
-plot(dattree[, -1], pch = ".")
-
-par(mfrow = c(2,4))
-hist(dattree$AvDurItem, breaks = 50, main = "")
-hist(dattree$NumItems, breaks = 50, main = "")
-hist(dattree$NumTopic, breaks = 50, main = "")
-hist(dattree$NumPopup, breaks = 50, main = "")
-hist(dattree$PropMoves, breaks = 50, main = "")
-hist(dattree$PathLinearity, breaks = 50, main = "")
-hist(dattree$Singularity, breaks = 50, main = "")
-hist(dattree$BetweenCentrality, breaks = 50, main = "")
-#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-
-#--------------- (2) Clustering ---------------
-
-df <- dattree[, -1] # remove case variable
-
-# Normalize Duration and SearchInfo
-df$AvDurItem <- normalize(df$AvDurItem)
-df$SearchInfo  <- normalize(df$SearchInfo)
-
-summary(df)
-
-# Look at collinearity
-cor_mat <- cor(df)
-diag(cor_mat) <- NA
-heatmap(cor_mat)
-
-#--------------- (2.2) Hierarchical clustering ---------------
-
-dist_mat <- cluster::daisy(df, metric = "gower")
-
-# "Flatten" with MDS
-coor_2d <- as.data.frame(cmdscale(dist_mat, k = 2))
-coor_3d <- as.data.frame(cmdscale(dist_mat, k = 3))
-# TODO: Better use MASS::isoMDS() since I am not using Euclidean distances?
-
-plot(coor_2d)
-rgl::plot3d(coor_3d)
-
-method <- c(average = "average", single = "single", complete = "complete",
-            ward = "ward")
-
-method <- "ward"
-
-hcs <- pbapply::pblapply(method, function(x) cluster::agnes(dist_mat, method = x))
-acs <- pbapply::pbsapply(hcs, function(x) x$ac)
-
-hc <- hcs$ward
-
-# Something like a scree plot (??)
-plot(rev(hc$height)[1:100], type = "b", pch = 16, cex = .5)
-
-k <- 4
-
-mycols <- c("#78004B", "#FF6900", "#3CB4DC", "#91C86E")
-
-cluster <- cutree(as.hclust(hc), k = k)
-
-table(cluster)
-
-plot(coor_2d, col = mycols[cluster])
-legend("topleft", paste("Cl", 1:4), col = mycols, pch = 21)
-rgl::plot3d(coor_3d, col = mycols[cluster])
-
-table(dattree[cluster == 1, "Pattern"])
-table(dattree[cluster == 2, "Pattern"])
-table(dattree[cluster == 3, "Pattern"])
-table(dattree[cluster == 4, "Pattern"])
-
-table(dattree[cluster == 1, "InfocardOnly"])
-table(dattree[cluster == 2, "InfocardOnly"])
-table(dattree[cluster == 3, "InfocardOnly"])
-table(dattree[cluster == 4, "InfocardOnly"])
-
-aggregate(. ~ cluster, df, mean)
-
-aggregate(cbind(duration, distance, scaleSize, rotationDegree, length,
-                nmove, nflipCard, nopenTopic, nopenPopup) ~ cluster, datcase,
-          mean)
-
-### Look at selected cases ###########################################
-tmp <- dat
-tmp$start <- tmp$date.start
-tmp$complete <- tmp$date.stop
-
-alog <- activitylog(tmp[tmp$case == 24016, ],
-                    case_id     = "case",
-                    activity_id = "item",
-                    resource_id = "path",
-                    timestamps  = c("start", "complete"))
-
-process_map(alog)
-
-rm(tmp)
-
-######################################################################
-
-res <- merge(dat, data.frame(case = dattree$case, cluster),
-             by = "case", all.x = TRUE)
-res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]
-
-xtabs( ~ item + cluster, res)
-aggregate(event ~ cluster, res, table)
-
-# Look at clusters
-par(mfrow = c(2, 2))
-vioplot::vioplot(duration ~ cluster, res)
-vioplot::vioplot(distance ~ cluster, res)
-vioplot::vioplot(scaleSize ~ cluster, res)
-vioplot::vioplot(rotationDegree ~ cluster, res)
-
-aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ cluster, res, mean)
-aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ cluster, res, median)
-
-write.table(res,
-            file = "results/haum/eventlogs_2019_case-clusters.csv",
-            sep = ";",
-            quote = FALSE,
-            row.names = FALSE)
-
-save(res, dist_mat, hcs, acs, datcase, dattree, coor_2d, coor_3d,
-     file = "results/haum/tmp_user-navigation.RData")
-
-#--------------- (3) Fit tree ---------------
-
-library(rpart)
-library(partykit)
-
-c1 <- rpart(as.factor(cluster) ~ ., data = dattree[, -1], method = "class")
-plot(as.party(c1))
-
-# with conditional tree
-c2 <- ctree(as.factor(cluster) ~ ., data = dattree[, -1], alpha = 0)
-plot(c2)
-
-#--------------- (4) Investigate variants ---------------
-
-res$start <- res$date.start
-res$complete <- res$date.stop
-
-alog <- activitylog(res,
-                    case_id     = "case",
-                    activity_id = "item",
-                    resource_id = "path",
-                    timestamps  = c("start", "complete"))
-
-trace_explorer(alog, n_traces = 25)
-# --> sequences of artworks are just too rare
-
-tr <- traces(alog)
-trace_length <- pbapply::pbsapply(strsplit(tr$trace, ","), length)
-tr[trace_length > 10, ]
-
-trace_varied <- pbapply::pbsapply(strsplit(tr$trace, ","), function(x) length(unique(x)))
-tr[trace_varied > 1, ]
-table(tr[trace_varied > 2, "absolute_frequency"])
-table(tr[trace_varied > 3, "absolute_frequency"])
-
-summary(tr$absolute_frequency)
-vioplot::vioplot(tr$absolute_frequency)
-
-# Power law for frequencies of traces
-tab <- table(tr$absolute_frequency)
-x <- as.numeric(tab)
-y <- as.numeric(names(tab))
-
-plot(x, y, log = "xy")
-p1 <- lm(log(y) ~ log(x))
-pre <- exp(coef(p1)[1]) * x^coef(p1)[2]
-lines(x, pre)
-
-
-# Look at individual traces as examples
-tr[trace_varied == 5 & trace_length > 50, ]
-# --> every variant exists only once, of course
-datcase[datcase$nitems == 5 & datcase$length > 50,]
-
-pbapply::pbsapply(datcase[, -c(1, 9)], median)
-
-#ex <- datcase[datcase$nitems == 4 & datcase$length == 15,]
-ex <- datcase[datcase$nitems == 5,]
-ex <- ex[sample(1:nrow(ex), 20), ]
-# --> pretty randomly chosen... TODO:
-
-case_ids <- NULL
-
-for (case in ex$case) {
-  if ("080" %in% res$item[res$case == case] | "503" %in% res$item[res$case == case]) {
-    case_ids <- c(case_ids, TRUE)
-  } else {
-    case_ids <- c(case_ids, FALSE)
-  }
-}
-
-cases <- ex$case[case_ids]
-
-
-for (case in cases) {
-
-  alog <- activitylog(res[res$case == case, ],
-                      case_id     = "case",
-                      activity_id = "item",
-                      resource_id = "path",
-                      timestamps  = c("start", "complete"))
-
-  dfg <- process_map(alog,
-                     type_nodes = frequency("absolute", color_scale = "Greys"),
-                     type_edges = frequency("absolute", color_edges = "#FF6900"),
-                     rankdir    = "LR",
-                     render     = FALSE)
-  export_map(dfg,
-             file_name = paste0("results/processmaps/dfg_example_cases_", case, "_R.pdf"),
-             file_type = "pdf",
-             title     = paste("Case", case))
-
-
-}
-
diff --git a/code/10_user-navigation.R b/code/10_user-navigation.R
new file mode 100644
index 0000000..29ff354
--- /dev/null
+++ b/code/10_user-navigation.R
@@ -0,0 +1,141 @@
+# 10_user-navigation.R
+#
+# content: (1) Load data
+#          (2) Clustering
+#          (3) Fit tree
+#          (4) Investigate variants
+#
+# input:  results/haum/event_logfiles_2024-02-21_16-07-33.csv
+# output: results/haum/eventlogs_pre-corona_case-clusters.csv
+#
+# last mod: 2024-03-08
+
+
+# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
+
+library(bupaverse)
+library(factoextra)
+library(rpart)
+library(partykit)
+
+#--------------- (1) Load data ---------------
+
+load("results/haum/dataframes_case_2019.RData")
+
+#--------------- (2) Clustering ---------------
+
+df <- dattree[, -1]
+
+summary(df)
+
+# Look at collinearity
+cor_mat <- cor(df)
+diag(cor_mat) <- NA
+heatmap(cor_mat)
+
+#--------------- (2.2) Hierarchical clustering ---------------
+
+dist_mat <- cluster::daisy(df, metric = "gower")
+
+# "Flatten" with MDS
+coor_2d <- as.data.frame(cmdscale(dist_mat, k = 2))
+coor_3d <- as.data.frame(cmdscale(dist_mat, k = 3))
+# TODO: Better use MASS::isoMDS() since I am not using Euclidean distances?
+
+plot(coor_2d)
+rgl::plot3d(coor_3d)
+
+method <- c(average = "average", single = "single", complete = "complete",
+            ward = "ward")
+
+method <- "ward"
+
+hcs <- pbapply::pblapply(method, function(x) cluster::agnes(dist_mat, method = x))
+acs <- pbapply::pbsapply(hcs, function(x) x$ac)
+
+hc <- hcs$ward
+
+# Something like a scree plot (??)
+plot(rev(hc$height)[1:100], type = "b", pch = 16, cex = .5)
+
+k <- 4
+
+mycols <- c("#78004B", "#FF6900", "#3CB4DC", "#91C86E")
+
+cluster <- cutree(as.hclust(hc), k = k)
+
+table(cluster)
+
+plot(coor_2d, col = mycols[cluster])
+legend("topleft", paste("Cl", 1:4), col = mycols, pch = 21)
+rgl::plot3d(coor_3d, col = mycols[cluster])
+
+table(dattree[cluster == 1, "Pattern"])
+table(dattree[cluster == 2, "Pattern"])
+table(dattree[cluster == 3, "Pattern"])
+table(dattree[cluster == 4, "Pattern"])
+
+table(dattree[cluster == 1, "InfocardOnly"])
+table(dattree[cluster == 2, "InfocardOnly"])
+table(dattree[cluster == 3, "InfocardOnly"])
+table(dattree[cluster == 4, "InfocardOnly"])
+
+aggregate(. ~ cluster, df, mean)
+
+aggregate(cbind(duration, distance, scaleSize, rotationDegree, length,
+                nmove, nflipCard, nopenTopic, nopenPopup) ~ cluster, datcase,
+          mean)
+
+### Look at selected cases ###########################################
+tmp <- dat
+tmp$start <- tmp$date.start
+tmp$complete <- tmp$date.stop
+
+alog <- activitylog(tmp[tmp$case == 24016, ],
+                    case_id     = "case",
+                    activity_id = "item",
+                    resource_id = "path",
+                    timestamps  = c("start", "complete"))
+
+process_map(alog)
+
+rm(tmp)
+
+######################################################################
+
+res <- merge(dat, data.frame(case = dattree$case, cluster),
+             by = "case", all.x = TRUE)
+res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]
+
+xtabs( ~ item + cluster, res)
+aggregate(event ~ cluster, res, table)
+
+# Look at clusters
+par(mfrow = c(2, 2))
+vioplot::vioplot(duration ~ cluster, res)
+vioplot::vioplot(distance ~ cluster, res)
+vioplot::vioplot(scaleSize ~ cluster, res)
+vioplot::vioplot(rotationDegree ~ cluster, res)
+
+aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ cluster, res, mean)
+aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~ cluster, res, median)
+
+write.table(res,
+            file = "results/haum/eventlogs_2019_case-clusters.csv",
+            sep = ";",
+            quote = FALSE,
+            row.names = FALSE)
+
+save(res, dist_mat, hcs, acs, datcase, dattree, coor_2d, coor_3d,
+     file = "results/haum/tmp_user-navigation.RData")
+
+#--------------- (3) Fit tree ---------------
+
+c1 <- rpart(as.factor(cluster) ~ ., data = dattree[, -1], method = "class")
+plot(as.party(c1))
+
+# with conditional tree
+c2 <- ctree(as.factor(cluster) ~ ., data = dattree[, -1], alpha = 0)
+plot(c2)
+
+
diff --git a/code/11_investigate-variants.R b/code/11_investigate-variants.R
new file mode 100644
index 0000000..5fae799
--- /dev/null
+++ b/code/11_investigate-variants.R
@@ -0,0 +1,101 @@
+# 11_investigate-variants.R
+#
+# content: (1) Read data
+#          (2) Extract characteristics for cases
+#          (3) Select features for navigation behavior
+#          (4) Export data frames
+#
+# input:  results/haum/event_logfiles_2024-02-21_16-07-33.csv
+# output: results/haum/eventlogs_pre-corona_case-clusters.csv
+#
+# last mod: 2024-03-08
+
+# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
+
+#--------------- (1) Read data ---------------
+
+load("results/haum/eventlogs_pre-corona_cleaned.RData")
+
+#--------------- (4) Investigate variants ---------------
+
+res$start <- res$date.start
+res$complete <- res$date.stop
+
+alog <- activitylog(res,
+                    case_id     = "case",
+                    activity_id = "item",
+                    resource_id = "path",
+                    timestamps  = c("start", "complete"))
+
+trace_explorer(alog, n_traces = 25)
+# --> sequences of artworks are just too rare
+
+tr <- traces(alog)
+trace_length <- pbapply::pbsapply(strsplit(tr$trace, ","), length)
+tr[trace_length > 10, ]
+
+trace_varied <- pbapply::pbsapply(strsplit(tr$trace, ","), function(x) length(unique(x)))
+tr[trace_varied > 1, ]
+table(tr[trace_varied > 2, "absolute_frequency"])
+table(tr[trace_varied > 3, "absolute_frequency"])
+
+summary(tr$absolute_frequency)
+vioplot::vioplot(tr$absolute_frequency)
+
+# Power law for frequencies of traces
+tab <- table(tr$absolute_frequency)
+x <- as.numeric(tab)
+y <- as.numeric(names(tab))
+
+plot(x, y, log = "xy")
+p1 <- lm(log(y) ~ log(x))
+pre <- exp(coef(p1)[1]) * x^coef(p1)[2]
+lines(x, pre)
+
+
+# Look at individual traces as examples
+tr[trace_varied == 5 & trace_length > 50, ]
+# --> every variant exists only once, of course
+datcase[datcase$nitems == 5 & datcase$length > 50,]
+
+pbapply::pbsapply(datcase[, -c(1, 9)], median)
+
+#ex <- datcase[datcase$nitems == 4 & datcase$length == 15,]
+ex <- datcase[datcase$nitems == 5,]
+ex <- ex[sample(1:nrow(ex), 20), ]
+# --> pretty randomly chosen... TODO:
+
+case_ids <- NULL
+
+for (case in ex$case) {
+  if ("080" %in% res$item[res$case == case] | "503" %in% res$item[res$case == case]) {
+    case_ids <- c(case_ids, TRUE)
+  } else {
+    case_ids <- c(case_ids, FALSE)
+  }
+}
+
+cases <- ex$case[case_ids]
+
+
+for (case in cases) {
+
+  alog <- activitylog(res[res$case == case, ],
+                      case_id     = "case",
+                      activity_id = "item",
+                      resource_id = "path",
+                      timestamps  = c("start", "complete"))
+
+  dfg <- process_map(alog,
+                     type_nodes = frequency("absolute", color_scale = "Greys"),
+                     type_edges = frequency("absolute", color_edges = "#FF6900"),
+                     rankdir    = "LR",
+                     render     = FALSE)
+  export_map(dfg,
+             file_name = paste0("results/processmaps/dfg_example_cases_", case, "_R.pdf"),
+             file_type = "pdf",
+             title     = paste("Case", case))
+
+
+}
+
diff --git a/code/R_helpers.R b/code/R_helpers.R
new file mode 100644
index 0000000..f543910
--- /dev/null
+++ b/code/R_helpers.R
@@ -0,0 +1,45 @@
+######################################################################
+time_minmax_ms <- function(subdata) {
+  subdata$min_time <- min(subdata$timeMs.start)
+    if (all(is.na(subdata$timeMs.stop))) {
+      subdata$max_time <- NA
+    } else {
+       subdata$max_time <- max(subdata$timeMs.stop, na.rm = TRUE)
+    }
+  subdata
+}
+
+######################################################################
+check_infocards <- function(subdata, artworks) {
+  infocard_only <- NULL
+  if(any(unique(subdata$item) %in% artworks)) {
+    infocard_only <- FALSE
+  } else {
+    infocard_only <- TRUE
+  }
+  as.numeric(infocard_only)
+}
+
+######################################################################
+normalize <- function(x) {
+  (x - min(x)) / (max(x) - min(x))
+}
+
+######################################################################
+get_centrality <- function(case, data) {
+
+  data$start <- data$date.start
+  data$complete <- data$date.stop
+
+  alog <- bupaR::activitylog(data[data$case == case, ],
+                             case_id     = "case",
+                             activity_id = "item",
+                             resource_id = "path",
+                             timestamps  = c("start", "complete"))
+
+  net <- processmapR::process_map(alog, render = FALSE)
+  inet <- DiagrammeR::to_igraph(net)
+
+  igraph::centr_betw(inet)$centralization
+}
+